Online nursing diagnosis and treatment system

Abstract

The invention relates to the technical field of nursing diagnosis and treatment, in particular to an online nursing diagnosis and treatment system, which comprises an interactive terminal used for checking nursing service items, nursing personnel information and appointment conditions and filling nursing requirements; the data matching module is used for carrying out preliminary matching on corresponding nursing service items and nursing personnel according to the ability portraits of the nursing personnel on the basis of the nursing requirements of the patients; the nursing recording module is used for recording execution data of nursing service items; according to the invention, based on intelligent matching of the nursing personnel capability portrait, optimal matching of the nursing task and the executor capability is ensured, and meanwhile, the execution data of the nursing personnel is regularly acquired through the risk prediction module, so that the risk prediction efficiency is improved. And path adjustment is carried out based on a professional nursing knowledge base, so that each intervention has a basis, and a nursing decision which most conforms to the current condition of the patient is made.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nursing diagnosis and treatment, in particular to an online nursing diagnosis and treatment system. BACKGROUND

[0002] With the deep integration of information technology and the medical and health field, online diagnosis and treatment systems emerge as the core carrier of medical digital transformation, which is driven by multiple factors such as social demand upgrading, technological innovation and policy environment support, and also faces the dual challenges of limitations of traditional medical model and existing technical bottlenecks.

[0003] In the traditional offline diagnosis and treatment mode, patients need to go through registration, waiting, examination, payment and other multi-link queuing, and a single visit often takes several hours, which is in sharp contrast to the needs of modern fast-paced life. For the elderly or critically ill patients who have difficulty in moving, the physical threshold of offline treatment is higher, which further exacerbates the difficulty of seeking medical treatment. In addition, the circulation of paper medical records limits the information exchange between different medical institutions, resulting in repeated examinations, diagnosis and other problems, which not only increases the burden of patients, but also reduces the quality of medical services.

[0004] In the event of a public health emergency, the shortcomings of offline diagnosis and treatment are particularly prominent. The traditional diagnosis and treatment mode is insufficient in response to long-term health management needs and cannot meet the needs of continuous monitoring and personalized guidance for patients with chronic diseases. SUMMARY

[0005] To solve the above problems, the present application provides an online nursing diagnosis and treatment system.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is:

[0007] An online nursing diagnosis and treatment system, comprising:

[0008] An interactive terminal for viewing nursing service items, nurse information, reservation status and filling in nursing requirements;

[0009] A data matching module for matching corresponding nursing service items and nurses based on the nursing requirements of patients and the ability portraits of nurses;

[0010] A nursing record module for recording the execution data of nursing service items;

[0011] A data storage module for storing nursing service items, nurse information, reservation status and execution data.

[0012] As a preferred, the data storage module further stores standardized nursing units, each of which corresponds to a preset nursing operation item and encapsulates the logic and resources required for executing the nursing operation item.

[0013] As preferred, the standardized nursing unit comprises:

[0014] An evaluation unit for guiding the patient or the nurse to evaluate the physical sign or the condition of the affected part;

[0015] An operation guidance unit for guiding the patient or the nurse to complete a specific nursing operation through multimedia;

[0016] A health education unit for pushing the patient with education materials related to the disease condition;

[0017] A monitoring reminder unit for setting a physiological parameter monitoring plan or a medication reminder.

[0018] As preferred, the patient-based nursing requirement is preliminarily matched with the nursing service item and the nursing staff according to the nursing staff capability profile, specifically comprising:

[0019] Obtaining capability profile data of a plurality of nursing staff from a nursing staff capability profile database, the capability profile data comprising skill tags, experience values, certification qualifications and user evaluation data;

[0020] Obtaining key requirement features in the nursing requirement, and calculating feature matching degrees of the key requirement features and each dimension in the capability profile data;

[0021] Based on a preset weight configuration, the feature matching degrees of each dimension are weighted and summed to obtain a comprehensive matching degree score of the nursing staff;

[0022] According to the comprehensive matching degree score from high to low, a target nursing staff set is screened out;

[0023] The nursing service item is preliminarily matched with the target nursing staff set.

[0024] As preferred, the calculation formula of the comprehensive matching degree score is:

[0025] Score = a CertMatch + β EvalMatch + γ SkillMatch + λ ExpMatch - μ Fatigue;

[0026] Wherein, Score is the matching degree score, CertMatch is the qualification certificate matching degree score, EvalMatch is the user comprehensive score, SkillMatch is the skill tag matching degree score, ExpMatch is the experience matching degree score, Fatigue is the fatigue degree score of the nursing staff in the recent time, a is the qualification score weight, β is the user evaluation weight, γ is the tag matching weight, λ is the experience matching weight, and μ is the fatigue matching weight.

[0027] As preferred, the construction of the caregiver ability portrait comprises:

[0028] Raw data of the caregivers are collected from multiple heterogeneous data sources, the raw data comprising basic information, skill certification information and historical evaluation data;

[0029] The collected raw data is cleaned, converted and normalized to form standardized data fields;

[0030] Based on the standardized data fields, a plurality of dimensional ability indicators are generated through a calculation model;

[0031] The plurality of dimensional ability indicators are aggregated to form a structured caregiver ability portrait;

[0032] Based on the basic information, skill certification information and service history data, multi-dimensional ability portrait data is generated.

[0033] As preferred, it further comprises a risk early warning module, the risk early warning module comprising:

[0034] Receiving physiological data of patients after nursing service, the physiological data comprising patient nursing operation records and patient subjective feedback;

[0035] Comparing and analyzing the physiological data with a preset nursing risk rule library to generate a risk assessment result;

[0036] When the risk assessment result exceeds a predetermined threshold, a risk early warning signal is generated and sent out;

[0037] In response to the risk early warning signal, based on a preset professional nursing knowledge base, an ongoing personalized nursing execution path is adjusted or supplemented to generate an adjusted nursing path.

[0038] As preferred, it further comprises a data interaction interface, the data interaction interface being used for communication connection with a medical information system.

[0039] As preferred, it further comprises a data visualization module, the data visualization module being used for visualizing processing of execution data of nursing service projects and patient physical data in an interactive terminal and generating a nursing report.

[0040] As preferred, it further comprises a continuing nursing management module, the continuing nursing management module being used for, at the end of nursing, automatically generating a personalized continuing nursing scheme through analysis based on historical nursing data and pushing the continuing nursing scheme to a patient end and an interactive terminal responsible for subsequent nursing.

[0041] The present application has the following advantages:

[0042] The application is based on intelligent matching of nursing staff ability portrait, ensures optimal matching of nursing tasks and performer ability, at the same time, regularly obtains execution data of nursing staff through a risk prediction module, and adjusts the path based on a professional nursing knowledge base, so that each intervention has a basis, thereby making a nursing decision most suitable for the current condition of the patient;

[0043] The application adopts the form of online reservation home nursing, so that the patient can obtain continuous and professional nursing guidance and supervision at home without frequent visits to the hospital, which greatly facilitates the elderly patients, chronic disease patients and postoperative rehabilitation patients who have difficulty in moving;

[0044] The system automatically completes task allocation, path pushing and data recording, reduces manual scheduling, telephone notification and paper record of nursing staff, realizes fine and data management, and significantly reduces the operation and management cost of the institution. BRIEF DESCRIPTION OF DRAWINGS

[0045] Fig. 1 The figure is a block diagram of an online nursing diagnosis and treatment system in specific embodiments of the application;

[0046] Fig. 2 The figure is a type diagram of a mini-program nursing service project in specific embodiments of the application;

[0047] Fig. 3 The figure is a mini-program reservation registration diagram in specific embodiments of the application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0049] Embodiment 1

[0050] Please refer to Figs. 1-3 The application relates to an online nursing diagnosis and treatment system, which comprises:

[0051] An interactive terminal is used for viewing nursing service projects, nursing staff information, reservation conditions and filling in nursing requirements, wherein the nursing requirements include required nursing service project types and / or expected nursing staff attributes;

[0052] A data matching module is used for preliminarily matching corresponding nursing service projects and nursing staff according to the nursing staff ability portrait based on the nursing requirements of the patient, and specifically comprises:

[0053] The capability profile data of multiple nursing staff are obtained from the nursing staff capability profile database. The capability profile data includes skill tags, experience values, certification qualifications and user evaluation data.

[0054] Obtain key demand features from nursing requirements and calculate the matching degree between key demand features and features of each dimension in the competency profile data;

[0055] Based on the preset weight configuration, the feature matching degree of each dimension is weighted and summed to obtain the comprehensive matching degree score.

[0056] Based on the nursing requirements and the competency profile data, a matching score for nursing staff is generated.

[0057] Based on the matching score, sort from high to low to select the target nursing staff set;

[0058] The nursing service items are initially matched with the target set of nursing staff;

[0059] Based on the initial matching results, patients can choose whether to accept the match on the interactive terminal. If they do not accept the match, they can manually select their preferred caregiver.

[0060] Specifically, the formula for calculating the matching score is as follows:

[0061] Score=α·CertMatch+β·EvalMatch+γ·SkillMatch+λ·ExpMatch-μ·Fatigue;

[0062] Wherein, Score is the matching score, CertMatch is the qualification certificate matching score, EvalMatch is the user's overall score, SkillMatch is the skill tag matching score, ExpMatch is the experience matching score, Fatigue is the nursing staff's fatigue score in the recent period, α is the qualification score weight, β is the user evaluation weight, γ is the tag matching weight, λ is the experience matching weight, and μ is the fatigue matching weight.

[0063] Further:

[0064] CertMatch = |t R ∩t P | / |t R |;

[0065] ExpMatch = 1 - (|E R -E P | / max(E R E P ));

[0066] SkillMatch = |SR ∩S P | / |S R ∪S P |;

[0067] EvalMatch = (Score P -Score min ) / (Score max -Score min );

[0068] where t R is the required qualification in the nursing requirement, t R is the actual qualification of the nursing staff, E R is the required experience level in the nursing requirement, E P is the actual experience level of the nursing staff, S R is the required skill label in the nursing requirement, S P is the actual skill label mastered by the nursing staff, EvalMatch is the comprehensive score of the required nursing staff in the nursing requirement, Score P is the actual evaluation average score of the nursing staff, Score max is the highest evaluation score of all nursing staff, Score min is the lowest evaluation score among all nursing staff.

[0069] Further, the construction of the nursing staff capability profile includes:

[0070] Collecting raw data of nursing staff from multiple heterogeneous data sources, the raw data including basic information, skill certification information and historical evaluation data;

[0071] Cleaning, converting and normalizing the collected raw data to form standardized data fields;

[0072] Based on the standardized data fields, generating multiple dimensional capability indicators through a calculation model;

[0073] Aggregating the multiple dimensional capability indicators to form a structured nursing staff capability profile;

[0074] Based on the basic information, skill certification information and service history data, generating multi-dimensional capability profile data.

[0075] A nursing record module for recording execution data of nursing service projects, wherein the execution data includes picture data and video stream data;

[0076] A data storage module for storing nursing service projects, nursing staff information, reservation information and execution data;

[0077] Further, the data storage module further stores standardized nursing units, each of the standardized nursing units corresponding to a preset nursing operation item and encapsulating logic and resources required for performing the nursing operation item, wherein the standardized nursing unit includes:

[0078] an evaluation unit for guiding a patient or a nurse to evaluate a sign or a condition of an affected part;

[0079] an operation guidance unit for guiding a patient or a nurse to complete a specific nursing operation through multimedia;

[0080] a health education unit for pushing education materials related to a disease to a patient;

[0081] a monitoring and reminding unit for setting a physiological parameter monitoring plan or a medication reminding.

[0082] Specifically, the risk early warning module further includes:

[0083] receiving physiological data of a patient after a nursing service, the physiological data including a nursing operation record of the patient and a subjective feedback of the patient;

[0084] comparing and analyzing the physiological data with a preset nursing risk rule library to generate a risk evaluation result;

[0085] generating and issuing a risk early warning signal when the risk evaluation result exceeds a predetermined threshold;

[0086] in response to the risk early warning signal, adjusting or supplementing an ongoing personalized nursing execution path based on a preset professional nursing knowledge base to generate an adjusted nursing path.

[0087] Further, in some other embodiments, the risk early warning module is further configured to perform risk prediction on execution data uploaded by a nursing staff, adopt existing picture or video stream processing technology, and receive picture data and video stream data uploaded by the nursing staff during execution of a nursing operation;

[0088] when the data is video stream data, picture data is extracted frame by frame, and the picture data and the video stream data are input into a pre-trained picture compliance analysis model for multi-dimensional compliance feature extraction and analysis;

[0089] based on the analysis result, generating a picture compliance score and identifying whether there is a violation item or a risk item;

[0090] when a violation item or a risk item is identified, triggering a corresponding risk early warning according to a severity thereof;

[0091] associating the risk early warning with a corresponding nursing execution path and pushing to a background management terminal.

[0092] Specifically, it further comprises a data interaction interface for communication connection with a medical information system;

[0093] Further, the data interaction interface is designed on the basis of the online nursing diagnosis and treatment system, and the data interaction between different programs of the standardized system and the data interaction with three-party systems, wherein the three-party systems specifically include: HIS: medical information system; LIS: medical examination system; PACS: medical examination system; EMR: electronic medical record system.

[0094] Specifically, it further comprises a data visualization module for visualizing the execution data of nursing services and patient physical data in the interactive terminal and generating a nursing report.

[0095] Specifically, it further comprises a continuing nursing management module for automatically generating a personalized continuing nursing plan based on historical nursing data at the end of nursing, and pushing the continuing nursing plan to the patient end and the interactive terminal responsible for subsequent nursing.

[0096] Embodiment 2

[0097] Scenario: Match a nurse responsible for daily health guidance for a newly diagnosed type II diabetes patient.

[0098] Patient care requirements: daily management of diabetes, insulin injection guidance, diet education.

[0099] Weight configuration (α = 0.4, β = 0.3, γ = 0.2, δ = 0.1): This type of nursing focuses on long-term guidance and experience, so the weights of skills and experience are higher.

[0100] Candidate comparison:

[0101] Nurse A:

[0102] SkillMatch: Skill tags include "diabetes education" and "insulin pen use", matching degree 0.9;

[0103] ExpMatch: 3 years of experience in endocrinology department, managed 150+ diabetes patients, matching degree 0.8;

[0104] CertMatch: Possesses "diabetes specialist nurse" certificate, matching degree 1.0;

[0105] EvalMatch: Historical evaluation 4.7 / 5.0, matching degree 0.94;

[0106] Comprehensive score:

[0107] Score = (0.4*0.9) + (0.3*0.8) + (0.2*1.0) + (0.1*0.94) = 0.36 + 0.24 + 0.20 + 0.094 = 0.894

[0108] Nurse B:

[0109] SkillMatch: Skill tag includes "diabetes education" but no "insulin pen use", match score 0.6;

[0110] EvalMatch: 1 year of general practice experience, managed 20 diabetes patients, match score 0.4;

[0111] CertMatch: No special certificates, match score 0;

[0112] EvalMatch: Historical evaluation 4.5 / 5.0, match score 0.9;

[0113] Overall score:

[0114] Score = (0.4*0.6) + (0.3*0.4) + (0.2*0) + (0.1*0.9) = 0.24 + 0.12 + 0 + 0.09 = 0.45;

[0115] Match result: The system prioritizes assigning the task to Nurse A. Because it is overall superior to Nurse B in key skills, experience, and qualifications, it can provide more professional service.

[0116] Example 3

[0117] Specifically, in the present application, the interactive terminal includes at least one of the following in the form of software:

[0118] Mobile application APP, installed on the operating system of a smart phone or tablet computer;

[0119] Mini-program, running in the container of a super-APP (such as WeChat or Alipay), without the need for independent installation;

[0120] Web web page application, accessed through a browser, suitable for desktop computers and mobile devices.

[0121] In the present application, reference is made to Figs. 2-3 As shown in the figure, taking the mini-program as an example, the home page of the mini-program is used to provide the following services:

[0122] Multi-channel reservation entry: Users can easily enter the reservation interface through the mobile APP or the mini-program of platforms such as WeChat and Alipay, without the need to download additional large applications, and the operation is simple and convenient. The reservation interface is designed to be simple and clear, and users can quickly find the required nursing service project.

[0123] Detailed service selection: In the applet, various types of nursing service items are listed in detail, such as PICC maintenance, muscle injection, intravenous infusion, wound care, rehabilitation guidance, etc. The content, applicable population, and precautions of each service item are explained in detail, making it convenient for users to choose according to their own needs.

[0124] Flexible time and place selection: When making an appointment, users can choose the appropriate service time according to their own schedule, and accurately fill in the service location, such as home address, nursing home address, etc. The system will match suitable nursing staff for the user according to the time and location selected by the user.

[0125] Dynamic information display: The applet displays the basic information of nurses, including name, photo, title, nursing items they are good at, service score, etc. Users can choose the nurses they are satisfied with according to these information. In addition, the platform will regularly train and examine nurses to continuously improve their professional competence and service level.

[0126] Personalized service: After the user makes an appointment for nursing service, the nurse will communicate with the patient through phone or online communication before going to the patient's home to understand the patient's condition, physical condition, medical history, etc. and assess the patient's nursing needs.

[0127] Customized nursing plan: Based on the assessment results, the nurse will develop a personalized nursing plan for the patient. For example, for patients who need PICC maintenance, the nurse will develop a targeted maintenance plan based on the type of catheter, the duration of catheterization, and the patient's skin condition. For elderly patients with chronic diseases, the nurse will provide comprehensive nursing services including medication guidance, dietary recommendations, and rehabilitation exercises based on the patient's condition.

[0128] Location service: Based on the service location filled in by the patient when making an appointment, the applet realizes one-key navigation function. After receiving the service order, the nurse can quickly and accurately reach the patient's location through the navigation system built in the applet, reducing the time wasted due to unfamiliarity with the route and improving service efficiency.

[0129] Real-time location sharing: During the nurse's journey to the service location, the patient can view the nurse's real-time location and estimated arrival time through the applet, making it easier for the patient to prepare in advance and improving the patient's waiting experience.

[0130] Medication package charging and medical insurance reimbursement

[0131] Medication package charging: The applet supports one-key medication package charging function. After the doctor issues a nursing service order based on the patient's condition, the relevant nursing service items will form a charging package. The patient can directly view the package content and cost on the applet and make one-key payment, simplifying the charging process.

[0132] Medical reimbursement docking: the applet realizes docking with the medical insurance system. For nursing service items that meet the medical reimbursement conditions, patients can directly choose medical payment when paying, and the system automatically calculates the medical reimbursement amount and personal self-payment amount, realizes the convenience of medical reimbursement, and reduces the economic burden of patients.

[0133] Further, when the nursing diagnosis and treatment system is developed by using the WeChat applet, it is divided into the following levels:

[0134] User layer: patients make appointment operations through the WeChat applet, and medical staff make nursing package issuing through the applet.

[0135] Application layer: including appointment management, service package management, file management and other modules.

[0136] Service layer: provides appointment interface, file record interface, data storage and other services.

[0137] Data layer: stores patient information, nursing record information, medical service data and the like.

[0138] Specifically, based on the data interaction interface described in Embodiment 1, WebAPI is adopted in the present application, WebAPI is a new framework for building HTTP services, which is used to dock various clients (browsers, mobile devices). On the.Net platform, WebAPI is an open source, ideal, and REST-ful service building technology. WebAPI is essentially a network application program interface, and the network application can use the API interface. The most important thing of WebAPI is that it can build services for various clients, and can realize storage services, message services, computing services and the like. By using these capabilities, powerful web applications can be developed.

[0139] Main functions of WebAPI

[0140] 1. Support CRUD (create, retrieve, update, delete) operations based on Httpverb (GET, POST, PUT, DELETE). Different http actions express different meanings, so there is no need to expose multiple APIs to support these basic operations.

[0141] 2. The reply to the request expresses different meanings through HttpStatusCode, and the client can negotiate the format with the server through the Accept header, for example, whether you want the server to return JSON format or XML format.

[0142] 3. The reply format of the request supports JSON, XML, and can be extended to add other formats.

[0143] 4. Native support for OData.

[0144] 5. Support for Self-host or IIHost.

[0145] 6. Support most of MVC features;

[0146] For example:

[0147] Routing / Controller / ActionResult / Filter / ModelBuilder / IOCContainer / Dependency Injection.

[0148] Embodiment 4

[0149] In response to the risk warning signal, based on the preset professional nursing knowledge base, the executing personalized nursing execution path is adjusted or supplemented, and an adjusted nursing path is generated, which includes, for example:

[0150] Example 1: Postoperative wound infection risk warning and path adjustment

[0151] Scenario: Patient home rehabilitation after knee replacement.

[0152] Original nursing path: report body temperature daily - upload wound photo every two days - perform rehabilitation training as planned.

[0153] Risk warning trigger: The wound photo uploaded by the patient through the app is analyzed by the system image recognition module, prompting "local swelling area increased by 30% compared with the previous day, with a small amount of exudation". At the same time, the patient self-reports a body temperature of 37.8℃. The system integrates these data and triggers a wound infection risk warning.

[0154] Path adjustment based on professional nursing knowledge base: An emergency wound assessment plan is immediately inserted into the original path, requiring the nurse to conduct remote review through video call within 1 hour, and an intensive body temperature monitoring plan is added, requiring the patient to report body temperature every 4 hours.

[0155] Replace the nursing unit: Replace the original "wound cleaning guidance" unit in the original plan with a "special disinfectant use and dressing change technology" unit for suspected infection, and conduct standardized teaching through video, automatically package and push the warning information and wound photo to the nursing staff.

[0156] Example 2: Low blood sugar risk warning and path adjustment for diabetic patients

[0157] Scenario: Diabetic patient, newly adjusted insulin dosage.

[0158] Original care path: daily morning and evening monitoring and reporting of blood glucose values - weekly summary of diet records.

[0159] Risk warning trigger: the patient's connected dynamic blood glucose meter transmits data in real time showing "current blood glucose value is 3.6 mmol / L and has shown a rapid downward trend in the past hour", the system compares with the risk rule library and triggers a "hypoglycemia emergency risk (high level)" warning.

[0160] Path adjustment based on professional nursing knowledge base:

[0161] Immediate supplementary care unit: force insertion of a "immediate meal 15g fast-acting carbohydrate food" care plan, and through voice broadcast, strongly remind the patient, and insert a 15-minute retest blood glucose care plan to confirm the treatment effect.

[0162] The system automatically generates a warning and simultaneously pushes it to the patient's family members' mobile phones and the community nurses' terminals, requesting assistance to confirm the patient's status.

[0163] Adjust the subsequent path: after the risk is resolved, the system automatically generates a "recent blood glucose fluctuation analysis report" and suggests that the nurse insert a "insulin dosage and diet review" special guidance care plan the next day to analyze the cause of hypoglycemia from the root.

[0164] The above embodiments only describe the preferred embodiments of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by ordinary engineering technicians in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. An online care system, comprising: The application relates to a nursing service system, which comprises the following parts: An interactive terminal for viewing nursing service items, nursing staff information, reservation information and filling in nursing requirements; A data matching module for matching corresponding nursing service items and nursing staff according to the nursing requirements of a patient and the ability portrait of a nursing staff; A nursing record module for recording the execution data of a nursing service item; A data storage module for storing nursing service items, nursing staff information, reservation information and execution data.

2. The online care diagnosis system according to claim 1, wherein, The data storage module also stores standardized nursing units, each of which corresponds to a preset nursing operation item and encapsulates the logic and resources required for executing the nursing operation item.

3. The online care diagnosis system according to claim 2, wherein The standardized nursing unit comprises: An evaluation unit for guiding a patient or a nurse to evaluate the signs or the condition of a diseased part; An operation guidance unit for guiding a patient or a nurse to complete a specific nursing operation through multimedia; A health education unit for pushing education materials related to a disease to a patient; A monitoring and reminding unit for setting a physiological parameter monitoring plan or a medication reminder.

4. The online care diagnosis system according to claim 1, wherein The matching of corresponding nursing service items and nursing staff according to the nursing requirements of a patient and the ability portrait of a nursing staff specifically comprises the following steps: Obtaining the ability portrait data of a plurality of nursing staff from an ability portrait database, wherein the ability portrait data comprises skill labels, experience values, certification qualifications and user evaluation data; Obtaining the key demand features in the nursing requirements, and calculating the feature matching degrees of the key demand features and each dimension in the ability portrait data; Based on a preset weight configuration, the feature matching degrees of each dimension are weighted and summed to obtain a comprehensive matching degree score of the nursing staff; According to the comprehensive matching degree score from high to low, a target nursing staff set is screened out; The nursing service items are preliminarily matched with the target nursing staff set.

5. The online care diagnosis system according to claim 4, wherein The calculation formula of the comprehensive matching degree score is as follows: Score = alpha.CertMatch + beta.EvalMatch + gamma.SkillMatch + lambda.ExpMatch - mu.Fatigue; Wherein Score is the matching degree score, CertMatch is the qualification certificate matching degree score, EvalMatch is the user comprehensive score, SkillMatch is the skill label matching degree score, ExpMatch is the experience matching degree score, Fatigue is the fatigue degree score of the nursing staff in the recent time, alpha is the qualification score weight, beta is the user evaluation weight, gamma is the label matching weight, lambda is the experience matching weight, and mu is the fatigue matching weight.

6. The online care diagnosis system according to claim 1, wherein, The construction of the ability portrait of the nursing staff comprises the following steps: Collecting the original data of the nursing staff from a plurality of heterogeneous data sources, wherein the original data comprises basic information, skill certification information and historical evaluation data; Cleaning, converting and normalizing the collected original data to form standardized data fields; Based on the standardized data fields, a plurality of dimension ability indexes are generated through a calculation model; The plurality of dimension ability indexes are aggregated to form a structured nursing staff ability portrait. Based on the basic information, skill certification information and service history data, multi-dimensional ability portrait data is generated.

7. The online care diagnosis system according to claim 6, wherein It also includes a risk warning module, which includes: Receiving physiological data of the patient after receiving the nursing service, the physiological data including patient care operation records and patient subjective feedback; Comparing and analyzing the physiological data with the preset nursing risk rule library to generate a risk assessment result; When the risk assessment result exceeds a predetermined threshold, a risk warning signal is generated and issued; In response to the risk warning signal, the executing personalized care execution path is adjusted or supplemented based on the preset professional care knowledge base, and an adjusted care path is generated.

8. The online care diagnosis system according to claim 7, wherein, It also includes a data interaction interface for communication connection with a medical information system.

9. The online care diagnosis system according to claim 8, wherein, It also includes a data visualization module for visualizing the execution data of the nursing service project and the patient's physical data in the interactive terminal and generating a nursing report.

10. The online care diagnosis system according to claim 9, wherein, It also includes a continuing care management module for automatically generating a personalized continuing care plan based on historical care data at the end of the care and pushing the continuing care plan to the patient end and the interactive terminal responsible for subsequent care.

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